This document discusses the challenges of legacy data systems and provides recommendations for addressing them. It notes that customer expectations around data access and use are increasing due to digital transformation trends. Legacy data systems often have fragmented, disconnected data stored in multiple formats with limited documentation. The document recommends three basic requirements to unlock the value of legacy data: understand the data by finding and documenting it, manage the data by defining governance and access rules, and exploit the data by building new capabilities. It emphasizes that data needs clear ownership, documentation, understanding of usage, and well-defined architecture. The best approach is often to co-exist with legacy systems by implementing a common access layer instead of a risky, costly migration.

1. The Ticking
Time Bomb of
Data
David Jones &
Norman Wren

2. Introductions
Digital Transformation – A Reality Check
The Ticking Time Bomb of Data
Q&A
1
2
3
4
Agenda

3. Introductions
3
1
David Jones
 VP Product Marketing
 Nuxeo
2
Norman Wren
 Former Technical and
Operations Director
 Santander

4. Introductions
4
David Jones
 VP of Product Marketing
 Nuxeo
 @InstinctiveDave
2
Norman Wren
 Former Technical and Operations Director
 Santander
Digital Transformation
A Reality Check

5. Digital Transformation
in Financial
Services
Massive Spend
Average $42M in 2018
Rising to $45M in 2019
Purpose
66% Customer facing
innovations
Success?
88% - project delayed, reduced
scope, or cancelled
26% - Digital Transformation =
Insurmountable Task
Statistics courtesy of Couchbase

6. “Everyone who hears these words of mine, and doesn't do them
will be like a foolish man, who built his house on the sand. The rain
came down, the floods came, and the winds blew, and beat on that
house; and it fell—and great was its fall.”
— Matthew 7:24–27

7. Building
on Sand?
Legacy Systems
Disconnected
Systems
Search not find
Customer Experience Mobile & Web Apps
Performance
& Scalability

8. The Ticking
time bomb
of Data
Making Sense of
Legacy Data
Norman Wren

9. Why is this
important?
Customer Expectation:
Customer in control
Unlimited data access
Always on 24 x 7
Real time
Added value services
Regulation :
Customer rights
Data portability
Open access to third
parties
Remediation of historic
practices
Internal Driver:
Exploit data assets

10. Legacy
Data
Challenges
• Fragmented data ; not real time; internal view;
unstructured data; access limited
• Multiple formats; limited documentation; Knowledge
gap
• Integrity within applications - not across
• Degradation over time
• Security and data leakage
• Obsolescence
• Compliance with Regulation
• Cost of Change; Time to market
• Consolidation of data stores.
• Architecture and technology compatibility
• BAU Running costs
• No scalability
• Access limitations
• Poor schema design
Access
Data Quality
and
Integrity
Risk
Cost
Performance

11. Value:
3 Basic
Requirements  Find Data
 Document attributes and meaning
 Understand usage and context
 Define Architecture
 Set usage, access and security rules
 Build governance and ownership
 Organise around common Business
Purposes
 Make accessible through common access
layer
 Use Meta data to organise and add value
 Build new Capabilities – Data Driven
Understand the
Data
Manage the
Data
Exploit the Data
3
1
2

12. Considerations Archaeology:
Find and document data and how used
Architecture:
Define data architecture and principles
Data ECO system:
Distributed data
Define data usage
Update; query; analytics
Common Layer:
To bridge technologies
Scaleability
Cloud processing; distributed data
Ownership
Governance and accountability

13. Key Points
Data as an Asset needs:
• Clear Ownership and Accountability
• Knowledge and documentation Meta data
• Clear Understanding of usage and value
• Data architects and engineers
• Architectural readiness
• Capability
• Data Strategy

14. Stick or
Twist?  Obsolescence
 Security
 Maintenance
 Cost
 Risk
 Cost
 Risk
 Data Quality
 Integrity
 Business case
 Less Risk
 Unlock assets
 Bridge old and new
 Create data eco system for the future
Do Nothing
Big Bang
Migration
Co-existence/
Common Layer

15. Summary Data knowledge and documentation
fundamental
• Hard work and time consuming
Clear target architecture addressing data
• technology stack and data usage
Common layer to separate out data from
business processing
Avoid migration if possible
• Avoid pitfall of access in situ
Define common Business purposes
Build road map
• Balance value, cost and risk
Invest in capability

16. Questions?

17. Thank you!
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